"""
Spatial Processing Configuration Types

This module defines configuration structures for spatial processing operations
including thinning, super-observation creation, and processing pipeline setup.
"""

using Dates

export ThinningConfig, SuperObservationConfig, ProcessingPipeline
export ThinningMethod, SuperObservationMethod
export SIMPLE_AVERAGE, QUALITY_WEIGHTED, ERROR_WEIGHTED, OPTIMAL_INTERPOLATION

"""
Enumeration of super-observation creation methods
"""
@enum SuperObservationMethod begin
    SIMPLE_AVERAGE = 1
    QUALITY_WEIGHTED = 2
    ERROR_WEIGHTED = 3
    OPTIMAL_INTERPOLATION = 4
end

"""
Configuration for observation thinning operations
"""
struct ThinningConfig{T<:AbstractFloat}
    # Spatial thinning parameters
    spatial_distance::T          # Minimum distance between observations (km)
    grid_spacing::T             # Grid spacing for regular thinning (degrees)
    adaptive_thinning::Bool     # Enable adaptive density-based thinning
    
    # Temporal thinning parameters
    temporal_interval::T        # Time interval for temporal thinning (hours)
    temporal_window::Period     # Time window for grouping observations
    
    # Quality-based selection
    quality_threshold::T        # Minimum quality threshold
    preserve_high_quality::Bool # Always preserve highest quality observations
    quality_weighting::Bool     # Use quality in selection process
    
    # Platform-specific settings
    platform_priorities::Dict{String, Int}  # Platform priority rankings
    max_per_platform::Dict{String, Int}     # Maximum observations per platform
    
    # Output limits
    max_observations::Int       # Maximum total observations after thinning
    target_density::T          # Target observation density (obs per degree²)
    
    # Processing options
    parallel_processing::Bool   # Enable parallel processing
    preserve_spatial_structure::Bool  # Maintain spatial coherence
    
    function ThinningConfig{T}(;
        spatial_distance = T(25.0),
        grid_spacing = T(0.25),
        adaptive_thinning = true,
        temporal_interval = T(6.0),
        temporal_window = Hour(3),
        quality_threshold = T(0.5),
        preserve_high_quality = true,
        quality_weighting = true,
        platform_priorities = Dict{String, Int}(),
        max_per_platform = Dict{String, Int}(),
        max_observations = 50000,
        target_density = T(4.0),
        parallel_processing = true,
        preserve_spatial_structure = true
    ) where T
        new{T}(spatial_distance, grid_spacing, adaptive_thinning,
               temporal_interval, temporal_window,
               quality_threshold, preserve_high_quality, quality_weighting,
               platform_priorities, max_per_platform,
               max_observations, target_density,
               parallel_processing, preserve_spatial_structure)
    end
end

"""
Configuration for super-observation creation
"""
struct SuperObservationConfig{T<:AbstractFloat}
    # Spatial aggregation parameters
    radius::T                   # Aggregation radius (km)
    min_observations::Int       # Minimum observations for super-ob
    max_observations::Int       # Maximum observations per super-ob
    
    # Combination method
    method::SuperObservationMethod  # Averaging method to use
    
    # Error treatment
    error_correlation_model::Symbol  # :exponential, :gaussian, :spherical
    correlation_length::T            # Spatial correlation length scale (km)
    representativeness_error::T      # Additional error for super-ob (same units as obs)
    error_inflation::T               # Error inflation factor
    
    # Quality control
    consensus_threshold::T      # Quality threshold for consensus
    quality_weighting::Bool     # Weight by observation quality
    outlier_detection::Bool     # Detect and exclude outliers
    outlier_threshold::T        # Standard deviations for outlier detection
    
    # Optimization parameters
    optimize_locations::Bool    # Optimize super-ob locations
    location_method::Symbol     # :centroid, :weighted_centroid, :median
    preserve_time_information::Bool  # Maintain temporal metadata
    
    # Validation settings
    validate_results::Bool      # Validate super-observations
    max_value_deviation::T      # Maximum allowed value deviation
    spatial_consistency_check::Bool  # Check spatial consistency
    
    function SuperObservationConfig{T}(;
        radius = T(50.0),
        min_observations = 3,
        max_observations = 10,
        method = QUALITY_WEIGHTED,
        error_correlation_model = :exponential,
        correlation_length = T(25.0),
        representativeness_error = T(0.1),
        error_inflation = T(1.2),
        consensus_threshold = T(0.7),
        quality_weighting = true,
        outlier_detection = true,
        outlier_threshold = T(3.0),
        optimize_locations = true,
        location_method = :weighted_centroid,
        preserve_time_information = true,
        validate_results = true,
        max_value_deviation = T(5.0),
        spatial_consistency_check = true
    ) where T
        new{T}(radius, min_observations, max_observations, method,
               error_correlation_model, correlation_length,
               representativeness_error, error_inflation,
               consensus_threshold, quality_weighting,
               outlier_detection, outlier_threshold,
               optimize_locations, location_method, preserve_time_information,
               validate_results, max_value_deviation, spatial_consistency_check)
    end
end

"""
Multi-stage processing pipeline configuration
"""
struct ProcessingPipeline{T<:AbstractFloat}
    # Pipeline stages
    stages::Vector{Function}    # Processing functions to apply
    configs::Vector{Any}        # Configuration for each stage
    stage_names::Vector{String} # Descriptive names for each stage
    
    # Processing options
    parallel::Bool              # Enable parallel processing
    validation::Bool            # Validate results at each stage
    stop_on_error::Bool        # Stop pipeline on first error
    
    # Memory management
    max_memory_usage::Int      # Maximum memory usage in MB
    garbage_collection::Bool   # Force GC between stages
    
    # Output control
    save_intermediate::Bool    # Save intermediate results
    output_directory::String   # Directory for intermediate outputs
    verbose_logging::Bool      # Enable detailed logging
    
    # Performance monitoring
    timing_enabled::Bool       # Track timing for each stage
    memory_monitoring::Bool    # Monitor memory usage
    
    function ProcessingPipeline{T}(stages::Vector{Function},
                                  configs::Vector{Any};
                                  stage_names = ["Stage $i" for i in 1:length(stages)],
                                  parallel = true,
                                  validation = true,
                                  stop_on_error = false,
                                  max_memory_usage = 2048,
                                  garbage_collection = true,
                                  save_intermediate = false,
                                  output_directory = ".",
                                  verbose_logging = false,
                                  timing_enabled = true,
                                  memory_monitoring = true) where T
        
        if length(stages) != length(configs)
            throw(ArgumentError("Number of stages must equal number of configs"))
        end
        
        if length(stage_names) != length(stages)
            throw(ArgumentError("Number of stage names must equal number of stages"))
        end
        
        new{T}(stages, configs, stage_names, parallel, validation, stop_on_error,
               max_memory_usage, garbage_collection, save_intermediate,
               output_directory, verbose_logging, timing_enabled, memory_monitoring)
    end
end

"""
Spatial index configuration for efficient neighbor searching
"""
struct SpatialIndexConfig{T<:AbstractFloat}
    # Index type
    index_type::Symbol         # :balltree, :kdtree, :grid, :hash
    
    # Distance metric
    distance_metric::Symbol    # :haversine, :euclidean, :manhattan
    earth_radius::T           # Earth radius in km for haversine distance
    
    # Performance tuning
    leaf_size::Int            # Leaf size for tree-based indices
    bucket_size::T            # Bucket size for spatial hashing (degrees)
    rebuild_threshold::Int    # Rebuild index after this many updates
    
    # Memory settings
    cache_distances::Bool     # Cache computed distances
    max_cache_size::Int      # Maximum cache entries
    
    function SpatialIndexConfig{T}(;
        index_type = :balltree,
        distance_metric = :haversine,
        earth_radius = T(6371.0),
        leaf_size = 10,
        bucket_size = T(1.0),
        rebuild_threshold = 1000,
        cache_distances = true,
        max_cache_size = 10000
    ) where T
        new{T}(index_type, distance_metric, earth_radius,
               leaf_size, bucket_size, rebuild_threshold,
               cache_distances, max_cache_size)
    end
end

"""
Create a standard thinning pipeline configuration
"""
function create_standard_thinning_pipeline(::Type{T}) where T<:AbstractFloat
    stages = [
        temporal_thinning,
        spatial_thinning,
        quality_filtering
    ]
    
    configs = [
        ThinningConfig{T}(temporal_interval=T(6.0)),
        ThinningConfig{T}(spatial_distance=T(25.0)),
        ThinningConfig{T}(quality_threshold=T(0.8))
    ]
    
    stage_names = [
        "Temporal Thinning",
        "Spatial Thinning", 
        "Quality Filtering"
    ]
    
    return ProcessingPipeline{T}(stages, configs; stage_names=stage_names)
end

"""
Create a standard super-observation pipeline configuration
"""
function create_standard_superob_pipeline(::Type{T}) where T<:AbstractFloat
    stages = [
        quality_filtering,
        create_super_observations,
        validate_super_observations
    ]
    
    configs = [
        ThinningConfig{T}(quality_threshold=T(0.6)),
        SuperObservationConfig{T}(radius=T(50.0), method=QUALITY_WEIGHTED),
        SuperObservationConfig{T}(validate_results=true)
    ]
    
    stage_names = [
        "Quality Pre-filtering",
        "Super-observation Creation",
        "Result Validation"
    ]
    
    return ProcessingPipeline{T}(stages, configs; stage_names=stage_names)
end

"""
Create a comprehensive processing pipeline with both thinning and super-observations
"""
function create_comprehensive_pipeline(::Type{T}) where T<:AbstractFloat
    stages = [
        temporal_thinning,
        spatial_quality_control,
        spatial_thinning,
        create_super_observations,
        optimize_observation_distribution,
        final_quality_check
    ]
    
    configs = [
        ThinningConfig{T}(temporal_interval=T(6.0)),
        ThinningConfig{T}(quality_threshold=T(0.5)),
        ThinningConfig{T}(spatial_distance=T(25.0), adaptive_thinning=true),
        SuperObservationConfig{T}(radius=T(40.0), method=ERROR_WEIGHTED),
        Dict("target_count" => 30000, "quality_weight" => 0.7),
        ThinningConfig{T}(quality_threshold=T(0.8))
    ]
    
    stage_names = [
        "Temporal Thinning",
        "Spatial Quality Control",
        "Adaptive Spatial Thinning",
        "Super-observation Creation",
        "Distribution Optimization",
        "Final Quality Check"
    ]
    
    return ProcessingPipeline{T}(stages, configs; stage_names=stage_names,
                                validation=true, verbose_logging=true)
end

# Placeholder functions referenced in pipeline creation
function spatial_thinning end
function quality_filtering end
function validate_super_observations end
function final_quality_check end

# Additional helper functions for spatial index management
"""
Build spatial index for efficient neighbor searching
"""
function build_spatial_index(observations::Vector{<:AbstractObservation{T}},
                           config::SpatialIndexConfig{T} = SpatialIndexConfig{T}()) where T
    
    if isempty(observations)
        return nothing
    end
    
    # Extract locations
    locations = Matrix{T}(undef, length(observations), 2)
    for (i, obs) in enumerate(observations)
        locations[i, 1] = obs.location.latitude
        locations[i, 2] = obs.location.longitude  
    end
    
    # Create appropriate index based on configuration
    if config.index_type == :balltree
        # Use appropriate distance metric
        if config.distance_metric == :haversine
            from NearestNeighbors import BallTree, Haversine
            return BallTree(locations', Haversine(config.earth_radius))
        else
            from NearestNeighbors import BallTree, Euclidean
            return BallTree(locations', Euclidean())
        end
    elseif config.index_type == :kdtree
        from NearestNeighbors import KDTree
        return KDTree(locations')
    else
        # Fallback to simple grid-based index
        return create_grid_index(locations, config)
    end
end

"""
Perform spatial search using appropriate index
"""
function spatial_search(index, center::Tuple{T,T}, radius::T) where T
    # Implementation depends on index type
    # This is a placeholder for the actual implementation
    return Int[]
end

"""
Create simple grid-based spatial index
"""
function create_grid_index(locations::Matrix{T}, config::SpatialIndexConfig{T}) where T
    # Simple grid-based index implementation
    # Returns a dictionary mapping grid cells to observation indices
    grid_index = Dict{Tuple{Int,Int}, Vector{Int}}()
    
    bucket_size = config.bucket_size
    
    for (i, location) in enumerate(eachrow(locations))
        lat, lon = location[1], location[2]
        grid_i = floor(Int, lat / bucket_size)
        grid_j = floor(Int, lon / bucket_size)
        
        key = (grid_i, grid_j)
        if haskey(grid_index, key)
            push!(grid_index[key], i)
        else
            grid_index[key] = [i]
        end
    end
    
    return grid_index
end